Problem Encoding Allowing Cheap Fitness Computation of Mutated Individuals

In the Evolutionary Computation field, it is frequent to assume that a computation load necessary for fitness value computation is, at least, similar for all possible cases. The main objective of this paper is to show that the above assumption is frequently false. Therefore, the examples of evolutionary methods that use problem encoding which allows for significant optimization of the fitness computation process are pointed out and analyzed. The definition of Problem Encoding Allowing Cheap Fitness Computation of Mutated Individuals (PEACh) is proposed. Another objective of the paper is to start a discussion concerning the computation load measurement in the evolutionary computation field. As shown, the Fitness Function Evaluation number is not always a fair measure and may be significantly affected by the quality of method implementation.

[1]  D. Goldberg,et al.  A Survey of Linkage Learning Techniques in Genetic and Evolutionary Algorithms , 2007 .

[2]  Michal Aibin,et al.  The evolutionary cost of Baldwin effect in the routing and spectrum allocation problem in elastic optical networks , 2017, Appl. Soft Comput..

[3]  Kalyanmoy Deb,et al.  Messy Genetic Algorithms: Motivation, Analysis, and First Results , 1989, Complex Syst..

[4]  Kalyanmoy Deb,et al.  RapidAccurate Optimization of Difficult Problems Using Fast Messy Genetic Algorithms , 1993, ICGA.

[5]  Kay Chen Tan,et al.  A Hybrid Estimation of Distribution Algorithm with Decomposition for Solving the Multiobjective Multiple Traveling Salesman Problem , 2012, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews).

[6]  Erik D. Goodman,et al.  An adaptive memetic framework for multi-objective combinatorial optimization problems: studies on software next release and travelling salesman problems , 2017, Soft Comput..

[7]  Yiqiao Cai,et al.  Differential evolution with hybrid linkage crossover , 2015, Inf. Sci..

[8]  Pawel B. Myszkowski,et al.  Hybrid ant colony optimization in solving multi-skill resource-constrained project scheduling problem , 2014, Soft Computing.

[9]  Kalyanmoy Deb,et al.  Hybrid gradient projection based Genetic Algorithms for constrained optimization , 2010, IEEE Congress on Evolutionary Computation.

[10]  Halina Kwasnicka,et al.  Multi Population Pattern Searching Algorithm: A New Evolutionary Method Based on the Idea of Messy Genetic Algorithm , 2011, IEEE Transactions on Evolutionary Computation.

[11]  D. Goldberg,et al.  BOA: the Bayesian optimization algorithm , 1999 .

[12]  Xin Yao,et al.  A New Approach for Analyzing Average Time Complexity of Population-Based Evolutionary Algorithms on Unimodal Problems , 2009, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[13]  Xin Yao,et al.  On the Easiest and Hardest Fitness Functions , 2012, IEEE Transactions on Evolutionary Computation.

[14]  Krzysztof Walkowiak,et al.  Evolutionary Algorithm for Congestion Problem in Connection-Oriented Networks , 2005, ICCSA.

[15]  Jing J. Liang,et al.  Problem Definitions and Evaluation Criteria for the CEC 2005 Special Session on Real-Parameter Optimization , 2005 .

[16]  Martin V. Butz,et al.  Hierarchical BOA on random decomposable problems , 2006, GECCO '06.

[17]  Krzysztof Walkowiak,et al.  Towards solving practical problems of large solution space using a novel pattern searching hybrid evolutionary algorithm - An elastic optical network optimization case study , 2015, Expert Syst. Appl..

[18]  Michal Wozniak,et al.  Optimizing distributed computing systems for k-nearest neighbours classifiers - evolutionary approach , 2011, Log. J. IGPL.

[19]  Kalyanmoy Deb,et al.  Sufficient conditions for deceptive and easy binary functions , 1994, Annals of Mathematics and Artificial Intelligence.

[20]  Pawel B. Myszkowski,et al.  Constructive heuristics for technology-driven Resource Constrained Scheduling Problem , 2015, 2015 Federated Conference on Computer Science and Information Systems (FedCSIS).

[21]  Krzysztof Walkowiak,et al.  Routing and spectrum allocation algorithms for elastic optical networks with dedicated path protection , 2014, Opt. Switch. Netw..

[22]  Krzysztof Walkowiak,et al.  Tabu search algorithm for routing, modulation and spectrum allocation in elastic optical network with anycast and unicast traffic , 2015, Comput. Networks.

[23]  Xiaodong Li,et al.  Cooperative Co-Evolution With Differential Grouping for Large Scale Optimization , 2014, IEEE Transactions on Evolutionary Computation.

[24]  Xin-She Yang,et al.  An improved discrete bat algorithm for symmetric and asymmetric Traveling Salesman Problems , 2016, Eng. Appl. Artif. Intell..

[25]  Donald R. Jones,et al.  A Taxonomy of Global Optimization Methods Based on Response Surfaces , 2001, J. Glob. Optim..

[26]  Krzysztof Walkowiak,et al.  Quasi-hierarchical Evolutionary Algorithm for Flow Optimization in Survivable MPLS Networks , 2007, ICCSA.

[27]  Oscar Montiel,et al.  Reducing the size of traveling salesman problems using vaccination by fuzzy selector , 2016, Expert Syst. Appl..

[28]  Xin Yao,et al.  Time complexity of evolutionary algorithms for combinatorial optimization: A decade of results , 2007, Int. J. Autom. Comput..

[29]  Michal Przewozniczek,et al.  Active Multi-Population Pattern Searching Algorithm for flow optimization in computer networks - The novel coevolution schema combined with linkage learning , 2016, Inf. Sci..

[30]  Stewart W. Wilson.  GA-Easy Does Not Imply Steepest-Ascent Optimizable , 1991, ICGA.

[31]  Alden H. Wright,et al.  Stability of Vertex Fixed Points and Applications , 1994, FOGA.

[32]  Hui Li,et al.  An Adaptive Evolutionary Multi-Objective Approach Based on Simulated Annealing , 2011, Evolutionary Computation.